How to Build an AI Startup: Go Big, Be Strange, Embrace Probable Doom

Earth, it’s said, is home to more than 10,000 AI startups. They’re more abundant than cheetahs. They outnumber dawn redwoods. The figure is a guess, of course—startups come, startups go. But last year, more than 2,000 of them got their first round of funding. As investors shovel their billions into AI, it’s worth asking: What are all these creatures of the boom doing?

I decided to approach as many recent AI founders as I could. The goal was not to try to pick winners but to see what it’s like, on the ground, to build AI products—how AI tools have changed the nature of their work; how terrifying it is to compete in a crowded field. It all sounded a bit like trying to tap-dance on the roiling surface of the sun. OpenAI rolls out an update, and a flurry of posts on X forecast the slaughter of a hundred startups. Brutal!

Is this a revolution that ends with so many engineers’ singed feet? For sure—they can’t all survive. A startup is an experiment, and most experiments fail. But run thousands of them across the economic landscape and you might just learn what the near future holds.

Navvye Anand is the cofounder of a company called Bindwell. When we got on a video call, he spoke with a half-smile and vaguely posh manner as he told me how he’s developing pesticides using custom AI models. Bindwell’s website once described these models as “insanely fast” and claimed that they could predict, in “mere seconds,” the results of experiments that would have taken days. Hearing Anand explain how he’s bringing the principles of AI drug discovery to crops, it was easy to forget that he’s 19.

Anand grew up in India reading Hacker News with his dad and was building his own large language models halfway through high school. Before he graduated, he, his cofounder (now 18), and two other friends from summer camp published a paper on bioRxiv, about an LLM they’d built to predict a facet of protein behavior. It got scientists buzzing on X. The paper was cited in a well-respected journal. They decided they should try to build a startup, brainstormed, and settled on protein-based pesticides. Then, the fairy tale continues, a wood sprite (sorry, venture capitalist) got in touch on LinkedIn and offered them $750,000 to drop out of high school and college and work on the company full-time. They accepted and got started. The teens knew next to nothing about agribusiness. That was last December.

Five months later, Anand and his cofounder opened their first biological testing lab in the San Francisco Bay Area, then moved to another, where they personally squeeze drops of promising molecules into tiny vials. (A protein-based compound can more precisely target a locust or aphid, goes the theory, and not also take out the humans, earthworms, bees.) I asked him how he’d picked up the skills to work in a wet lab. “I hired a friend,” he said cheerfully. The friend coached him over the summer before heading back to college in the fall. “Now I can do some biochemical assays,” Anand says. “Not like a whole range of assays, but basic, wet-lab validation of our models.”

Huh, I thought. That a few teens had in a handful of months built their own LLMs, learned the biochemistry of pest control, used their models to identify potential molecules, and were now pipetting away in their own lab seemed not shabby. In truth, once I’d tallied up all that they’d done, it struck me as completely absurd. I had expected to hear that AI tools are speeding up parts of building a company, but I had only a vague sense of the scale of their impact. So in my next interview, with the cofounders of a 14-month-old startup called Roundabout Technologies, I got straight to that: Break down what’s changed and by how much.